| Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1 M Courbariaux, I Hubara, D Soudry, R El-Yaniv, Y Bengio arXiv preprint arXiv:1602.02830, 2016 | 3447 | 2016 |
| Binarized neural networks I Hubara, M Courbariaux, D Soudry, R El-Yaniv, Y Bengio Advances in neural information processing systems 29, 2016 | 2444 | 2016 |
| Quantized neural networks: Training neural networks with low precision weights and activations I Hubara, M Courbariaux, D Soudry, R El-Yaniv, Y Bengio Journal of Machine Learning Research 18 (187), 1-30, 2018 | 2117 | 2018 |
| Train longer, generalize better: closing the generalization gap in large batch training of neural networks E Hoffer, I Hubara, D Soudry Advances in neural information processing systems 30, 2017 | 922 | 2017 |
| Mlperf inference benchmark VJ Reddi, C Cheng, D Kanter, P Mattson, G Schmuelling, CJ Wu, ... 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 439 | 2020 |
| Scalable methods for 8-bit training of neural networks R Banner, I Hubara, E Hoffer, D Soudry arXiv preprint arXiv:1805.11046, 2018 | 363 | 2018 |
| Expectation backpropagation: Parameter-free training of multilayer neural networks with continuous or discrete weights D Soudry, I Hubara, R Meir Advances in neural information processing systems 27, 2014 | 293 | 2014 |
| Augment your batch: Improving generalization through instance repetition E Hoffer, T Ben-Nun, I Hubara, N Giladi, T Hoefler, D Soudry Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 217 | 2020 |
| Accurate post training quantization with small calibration sets I Hubara, Y Nahshan, Y Hanani, R Banner, D Soudry International Conference on Machine Learning, 4466-4475, 2021 | 204* | 2021 |
| Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or− 1. arXiv 2016 M Courbariaux, I Hubara, D Soudry, R El-Yaniv, Y Bengio arXiv preprint arXiv:1602.02830 33, 0 | 110 | |
| Fix your classifier: the marginal value of training the last weight layer E Hoffer, I Hubara, D Soudry arXiv preprint arXiv:1801.04540, 2018 | 104 | 2018 |
| The knowledge within: Methods for data-free model compression M Haroush, I Hubara, E Hoffer, D Soudry Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 103 | 2020 |
| Mlperf inference benchmark. In 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA) VJ Reddi, C Cheng, D Kanter, P Mattson, G Schmuelling, CJ Wu, ... IEEE, 2020 | 74 | 2020 |
| Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks I Hubara, B Chmiel, M Island, R Banner, J Naor, D Soudry Advances in neural information processing systems 34, 21099-21111, 2021 | 73 | 2021 |
| Augment your batch: better training with larger batches E Hoffer, T Ben-Nun, I Hubara, N Giladi, T Hoefler, D Soudry arXiv preprint arXiv:1901.09335, 2019 | 69 | 2019 |
| Quantized neural network training and inference ELY Ran, I Hubara, D Soudry US Patent 10,831,444, 2020 | 64 | 2020 |
| Deep unsupervised learning through spatial contrasting E Hoffer, I Hubara, N Ailon arXiv preprint arXiv:1610.00243, 2016 | 29 | 2016 |
| Mix & match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency E Hoffer, B Weinstein, I Hubara, T Ben-Nun, T Hoefler, D Soudry arXiv preprint arXiv:1908.08986, 2019 | 23 | 2019 |
| Playing SNES in the retro learning environment N Bhonker, S Rozenberg, I Hubara arXiv preprint arXiv:1611.02205, 2016 | 23 | 2016 |
| Minimum variance unbiased n: M sparsity for the neural gradients B Chmiel, I Hubara, R Banner, D Soudry The Eleventh International Conference on Learning Representations, 2022 | 12* | 2022 |
Daniel SoudryAssociate ProfessorBestätigte E-Mail-Adresse bei technion.ac.il
